WO2008103969A1 - Structure de données d'auto-description - Google Patents

Structure de données d'auto-description Download PDF

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Publication number
WO2008103969A1
WO2008103969A1 PCT/US2008/054812 US2008054812W WO2008103969A1 WO 2008103969 A1 WO2008103969 A1 WO 2008103969A1 US 2008054812 W US2008054812 W US 2008054812W WO 2008103969 A1 WO2008103969 A1 WO 2008103969A1
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WO
WIPO (PCT)
Prior art keywords
data
self
describing
component
core
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PCT/US2008/054812
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English (en)
Inventor
Chris Demetrios Karkanias
Stephen Edward Hodges
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Microsoft Corporation
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Publication of WO2008103969A1 publication Critical patent/WO2008103969A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Definitions

  • a relational database refers to a data storage mechanism that employs a relational model in order to interrelate data. These relationships are defined by a set of tuples that all have a common attribute. The tuples are most often represented in a two-dimensional table, or group of tables, organized in rows and columns.
  • DMM data-mining model
  • the innovation disclosed and claimed herein in one aspect thereof, comprises a system that can enable establishment of a self-describing data network.
  • the data network maintains health-related data where each element includes a core data element wrapped with descriptive metadata.
  • the descriptive metadata e.g.
  • tags can be employed to interrelate the data elements as well as to facilitate efficient traversal of the data network as a whole.
  • the innovation provides a mechanism by which data can be collected, validated and stored in such a way that permits each data element to be inherently self-describing. This self-describing property can enhance and optimize usability of the data network in accordance with operations such as data mining, querying, etc.
  • health-related data can be drilled down into the smallest meaningful component and subsequently surrounded with metadata that describes the nature of the data as well as how to interact with the data. This data arrangement can enable information to emerge out of a suitably organized data set.
  • This data set can be viewed as 'simultaneously relational' because the metadata enables relationships to be established just-in-time as needed and/or desired. Moreover, this data set can leverage the power of a network of data by establishing relationships on-the-fly.
  • the self-describing data elements of the innovation can be maintained within a pool, or 'soup', of data that can be organized in such a way that arbitrary paths can be established just-in-time.
  • the subject innovation enables organization of the captured data such that a user can traverse large areas of the data set without having a predetermined data model.
  • the data model can be established just-in-time.
  • metadata tagged to captured data can allow all data to exist in, and to be extracted as needed/desired from, a single pool.
  • metadata driving the just-in-time pattern assembly in combination with mathematical principles is can be possible to traverse a network of an arbitrarily large size in a finite number of steps. This is particularly useful as relationships between information in the pool can be established just-in-time in only a few operations despite its vast size.
  • the Hubert space allows for mathematical treatment of operating on multidimensional data sets in arbitrary space.
  • the innovation enables data to be manipulated in large (e.g., 50, 100, 1000) dimensional graphs.
  • large (e.g., 50, 100, 1000) dimensional graphs e.g., 50, 100, 1000) dimensional graphs.
  • the vector within the space is finite regardless of the number of dimensions employed.
  • an infinite number of points will most likely not be available within the pool
  • one feature of the innovation is that operations can be performed upon the data in order to establish relationships just-in-time regardless of the number of data points.
  • the mere storage of the data in this graph space is also the query. Thus, in effect, storage of the data produces the result.
  • FIG. 1 illustrates a system that establishes a self-describing health-related data network in accordance with an aspect of the innovation.
  • FIG. 2 illustrates an example self-describing health-care data network having N dimensions in accordance with an aspect of the innovation.
  • FIG. 3 illustrates an exemplary flow chart of procedures that facilitate collection, validation and storage of health-related data in accordance with an aspect of the innovation.
  • FIG. 4 illustrates an exemplary flow chart of procedures that facilitate data collection in accordance with an aspect of the innovation.
  • FIG. 5 illustrates an exemplary flow chart of procedures that facilitate data validation in accordance with an aspect of the innovation.
  • FIG. 6 illustrates an exemplary flow chart of procedures that facilitate data storage in accordance with an aspect of the innovation.
  • FIG. 7 illustrates an alternative block diagram of an example system that facilitates generation of a self-describing health-related data network.
  • FIG. 8 illustrates an example data organization component that facilitates data collection, validation and storage in accordance with an aspect of the innovation.
  • FIG. 9 illustrates a detailed block diagram of an example data organization component in accordance with an aspect of the innovation.
  • FIG. 10 illustrates an alternative block diagram of a data organization component that includes machine learning and reasoning (MLR) component that can automate functionality in accordance with an aspect of the innovation.
  • FIG. 11 illustrates a block diagram of a computer operable to execute the disclosed architecture.
  • MLR machine learning and reasoning
  • FIG. 12 illustrates a schematic block diagram of an exemplary computing environment in accordance with the subject innovation.
  • a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer.
  • a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer.
  • an application running on a server and the server can be a component.
  • One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers.
  • the term to "infer” or “inference” refer generally to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic-that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources.
  • FIG. 1 illustrates a system 100 that enables data to be self-describing such that the data need not be stored within a specified pre-defined structure (e.g., relational database).
  • a specified pre-defined structure e.g., relational database
  • system 100 includes an interface layer 102 that provides a gateway between a source or origin of data and a self-describing health-related data network 104, hereinafter referred to as data network 104.
  • the data network 104 can include 1 to N data elements 106, where N is an integer.
  • data network 104 can be N-dimensional in structure whereby the structure can constantly change in accordance with stored data.
  • system 100 can provide ways to capture and leverage information in the health care and education spaces. For example, many of the ideas presented facilitate ways to improve health diagnosis and treatment as well as to assist in the promotion of healthy living.
  • consumer orientation is an important aspect to a solution in this area.
  • these services can provide means to enable users to navigate through the health and wellness states.
  • the system 100 can address enablement of an appropriate data platform to create a paradigm shift that makes the health care system compete on value as opposed to competing on cost.
  • the innovations described herein address how to deliver maximum value where scale of the data platform (e.g., data network 104) provides for economic reduction of cost. Effectively, integrated data can provide for activating changes in behaviors of persons - awareness is half of the battle.
  • the subject innovation addresses aspects of the information supply chain that include collection, validation, and storage of self-describing data elements 106.
  • the self-describing data elements 106 can be structured in such a way that they are wrapped (or tagged) with metadata that defines detailed attributes about the core data item. This concept can be better understood as illustrated in FIG. 2.
  • each data element 106 within the data network 104 can include a core data element 202 and one or more attributes or descriptive data elements 204 (e.g., tag(s)).
  • the conventional canonical way of working with data was to develop a model or defined structure/framework thereafter collecting and storing data in accordance with the predefined model, structure or framework. This conventional method of organizing data can limit use as conventional categories had to be predetermined in order to know what to collect and where to store what has been collected.
  • each of the data elements 106 include information (e.g., tags 204) that inherently describes the nature, origin, substance, context, relationship, etc. of the core data element 202.
  • information e.g., tags 204
  • the information mechanics of the data network 104 enable relationships to be made on- the-fly or just-in-time dynamically without the need for any predefined model.
  • data networks e.g., data network 104 having a vast number of data elements (e.g., millions, billions) can be traversed with a limited number of hops (e.g., 10) in order establish and subsequently leverage interconnectivity between the elements.
  • known principles and algorithmic techniques such as 'Small World' theorem or analysis can be employed to illustrate the ability to traverse such a vast amount of data in such a limited number of hops.
  • Small World concepts teach that, from statistical physics, a large class of complex networks characterized by high clustering properties includes incredibly short paths between pairs of nodes (e.g., data elements 106). Further, this ability to traverse a complex network can also be explained analogously with the concept of 'Six Degrees of Separation.' This concept has been demonstrated in areas ranging from acquaintances between individuals in the United States, to telephone call graphs, to data packet (e.g., email) delivery via the worldwide web (e.g., Internet).
  • data packet e.g., email
  • the data network 104 of FIG. 2 illustrates data elements 106 that resemble atoms as used in chemistry.
  • the core data element 202 can be representative of a smallest sensible bit of information imaginable that is wrapped with metadata (e.g., tags 204) that describe the atomized bit.
  • metadata e.g., tags 204
  • the data element 106 is representative of John Doe's blood pressure measurement.
  • the core data element 202 can be representative of a systolic pressure measurement which represents the maximum pressure in an artery at the moment when the heart is beating and pumping blood through the body.
  • another core data element 202 can be representative of a corresponding diastolic pressure measurement which is the lowest pressure in an artery in the moments between beats when the heart is resting.
  • these core data elements 202 can be merely a numerical value where descriptive attributes, e.g., tags 204, can be associated to describe and interrelate the data.
  • tags 204 can be attached that defines meaning of the value (e.g., blood pressure measurement), for example, the units of measurement for the numerical value (e.g., millimeters of mercury (mmHg)), the source/origin of the measurement, the method of reading, time/date of reading, patient context when reading was taken, relationships to other blood pressure measurements as well as other medical records, how to interact with the measurement, interesting issues relating to the measurement, etc.
  • the granularity of the tags can be a function of most any criteria including, but not limited to, user preference, industry standards, corporate regulations, governmental regulations, inference, etc.
  • pivots can also be constructed upon information stored as attributes (e.g., tags 204) relating to core data items 202.
  • attributes e.g., tags 204
  • traversal could hop from John Doe's blood pressure measurement, to patient Jim, to nurse Jane, to other blood pressure measurements administered by nurse Jane.
  • this traversal can be analogized to TCP/IP (transmission control protocol/Internet protocol) which is a routable communications protocol for the Internet.
  • TCP/IP transmission control protocol/Internet protocol
  • packet headers include source and destination information such that a packet can traverse the Internet subsequently arriving at a desired target location.
  • the tags 204 can include this information in a suitably standard format that defines how metadata is collected and wrapped to core data elements 202.
  • the health-related data network 104 can be structured in such a way that it is effectively an N-dimensional data structure, where N is an integer.
  • vectors can be drawn between data elements having the same or similar characteristics (e.g. , tags 202) such that interconnectivity can easily be identified to facilitate pattern and trend identification for the purposes of health-related matters.
  • the Tridimensional health-related data network 104 can enable the data to be analyzed and/or shared thereby establishing an efficient and intelligent system of data sharing as applied to health-related matters. It is to be understood and appreciated that, in aspects, the system 100 of FIG. 1 essentially enables a third party (or group of third parties) to maintain health-related data which can be easily shared and intelligently mined to assess health-related topics.
  • FIG. 3 illustrates a methodology of managing health-related data in accordance with an aspect of the innovation. While, for purposes of simplicity of explanation, the one or more methodologies shown herein, e.g., in the form of a flow chart, are shown and described as a series of acts, it is to be understood and appreciated that the subject innovation is not limited by the order of acts, as some acts may, in accordance with the innovation, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all illustrated acts may be required to implement a methodology in accordance with the innovation.
  • data elements can be collected for example, health-related data elements 106 of FIG. 1 can be gathered. In operation, these data elements can be automatically and/or dynamically collected in most any manner ranging from push/pull from sensor technologies, applications, user-initiated actions or the like. In one example, image recorders, medical instruments, etc. can be equipped to automatically transmit data by way of an interface (e.g., 102 of FIG.1). [0043] Once collected, at 304, the data can be validated with regard to most any desired factor(s), for example, completeness, integrity, value, etc. Additionally, at 306, the data can be maintained within a storage mechanism for subsequent retrieval, access, processing or use. Although a specific ordering of acts is illustrated in FIG. 3, it is to be understood that, where possible, the acts can be enacted in alternative orders. For instance, data can be validated either before or after actual collection and/or storing. These alternative aspects are to be included within the scope of this disclosure and claims appended hereto.
  • a data source can be monitored, for example, physiological and/or environmental sensors can be actively monitored by which to capture data. Similarly, most any data source can be actively monitored to capture data, including but not limited to, financial trading markets, insurance markets, broadcast ratings, traffic patterns, etc.
  • the data can be received by way of pushing and/or pulling the data from the source or origin.
  • the data can be de-constructed at 406 which effectively can separate the data element into the smallest sensible information bit ⁇ e.g., core data element 202 of FIG. 2).
  • a determination is made at 408 to establish if the smallest sensible bit has been determined.
  • the data is further de-constructed at 406 as shown. If so, metadata that describes the smallest sensible bit ⁇ e.g., core data element 202 of FIG. 2) can be gathered. It is to be understood that this metadata can be gathered by way of the de-construction process at 406 or alternatively, by way of subsequent information gathering processes ⁇ e.g., 410).
  • data is received, for example, data can be received in most any manner from most any source/origin. As described above, data can be pushed or pulled from a source in accordance with aspects of the innovation.
  • accuracy of the data can be validated.
  • the accuracy can validated by employing a policy or threshold to compare the captured value to an industry standard range, historical patient data, statistical demographic values, etc. Essentially, accuracy confidence can be increased as a function of some predetermined or preprogrammed rules, inference, threshold or benchmark. In addition to the value itself, other factors can contribute to the validation process thereby increasing confidence levels. By way of example, experience of the health care professional, age of measuring device, similarity to previous measurements, etc. can all be considered to increase the validation confidence.
  • the origin of the data can also be validated in order to identify and/or discover any possibility of incorrect or contaminated data entering the network. This process can be analogized to spam filtering of emails.
  • white and black lists can be managed in order to permit data to enter the network.
  • FIG. 6 there is illustrated an example methodology of storing data (e.g., act 306 of FIG. 3).
  • data elements can be analyzed. Accordingly, relationships of a subject data element to other data elements can be established at 604. In other words, implicit, previously unknown, and potentially useful information can be identified from the data element as a function of the data network.
  • a policy and/or rule can be applied in the analysis which can discern or recognize patterns and/or correlations amongst the stored health- related data to the subject data element.
  • a single or combination of analysis techniques can be employed including, without limitation, statistics, regression, neural networks, decision trees, Bayesian classifiers, Support Vector Machines, clusters, rule induction, nearest neighbor and the like to locate hidden knowledge within data.
  • a model can be built and trained in accordance with a type of data. Subsequently, the trained model can be employed to identify patterns and/or correlations of future elements of the same or similar type.
  • storage specifics are determined. For instance, optimal clustering techniques can be identified. As described supra, these clustering techniques can enhance the effectiveness of Small World analysis techniques of traversing the network. Once storage specifics are determined, the self-describing data can be stored within the self-describing data network.
  • FIG. 7 illustrates yet another alternative block diagram of system 100 in accordance with an aspect of the innovation.
  • data network 104 can include most any type of data elements 106 known in the art.
  • data elements 106 can include, but are not limited to, conventional file folders that maintain documents and data, stand-alone documents, core data items tagged with metadata, disparate storage devices and/or relational database tables, as well as any combination thereof.
  • data network 104 is illustrated as a single component, the network can be distributed within various clouds, enterprises, machines, etc. without departing from the spirit and/or scope of the innovation.
  • FIG. 7 illustrates that data elements 106 can be obtained from most any source/origin including, but not limited to, 1 to M applications or 1 to P users where M and P are integers by way of a data organization component 702.
  • the users can be equipped with image recorder components (not shown) that can effectively capture a sequence of images that correspond to a user event.
  • image recorder components not shown
  • other physiological and/or environmental sensory mechanisms can be employed that can dynamically push data to the network where it can be collected, validated and stored.
  • FIG. 8 illustrates a block diagram of data organization component 702 in accordance with an aspect of the innovation.
  • data organization component 702 can include a collection component 802, a validation component 804 and a storage component 806. Functionalities of each of these components have been described with reference to the figures above.
  • the collection component 802 can facilitate either pulling or receiving pushed data from origins and/or sources.
  • the collection component 802 can automatically poll sensory mechanisms to populate the self-describing data network ⁇ e.g., 104 of FIG. 1).
  • blood pressure readings can be automatically collected by the collection component 802 for storage within the data network.
  • the validation component 804 can facilitate at least two safeguards related to the integrity of the data network. First, the validation component 804 can validate the accuracy of the received data to detect any data issues related to the element in general as well as the transmission/reception of the data. Additionally, the validation component 804 can validate ⁇ e.g., authenticate) the source/origin of the data element. In this manner, the validated source/origin can be used to tag the core data element as described above as well as to potentially filter incoming data. For instance, white and/or black list filtering can be used to prohibit potentially bad actors from populating the data network. [0057] The storage component 806 can facilitate data analysis that identifies relationships between a subject data element and those data elements maintained within the data network. This relationship data can be employed to facilitate clustering and/or logical/intelligent placement of data elements. It will be appreciated that proactive clustering can enhance usability of the data network when traversing to identify specific element types, patterns, trends, etc.
  • FIG. 9 illustrates a more detailed block diagram of an example data organization component 702 in accordance with an embodiment of the innovation.
  • this component can include a data atomizing component 902 and an attribute collection component 904.
  • the data atomizing component 902 can segregate received data elements into the smallest sensible bit of information together with any identifying or descriptive information.
  • the attribute collection component 904 can further gather additional descriptive information which can be incorporated into the data element structure ⁇ e.g. , core data element wrapped with descriptive metadata).
  • the subcomponents (902, 904) of the collection component 802 facilitate generation of the self-describing data elements (e.g., 106 of FIG. 1).
  • the validation component 804 can include an accuracy validation component 906 and an origin validation component 908. Each of these components (906, 908) can be employed to minimize and/or eliminate the possibility of populating the data network with incorrect, useless or contaminated data.
  • the accuracy validation component 906 can be employed to intelligently assess the received data element by determining what the data should be versus what it is. For example, if the data element represents a blood pressure measurement, it will be appreciated that this measurement has a defined range that corresponds to this type of measurement. As such, the accuracy validation component 906 can verify that the measurement falls within the range of values for this type of data.
  • the origin validation component 908 can further be used to self police the data that enters the data network.
  • the source and/or origin of each data element can be validated and if desired, subjected to a filtering mechanism (e.g., white/black list) that can effectively prohibit data from predefined sources.
  • a filtering mechanism e.g., white/black list
  • This white/black list technique is but one example of how the source/origin information can be employed to enhance the quality of data within the data network.
  • Other examples include, tester/health care professional qualifications, age of testing equipment, location of origination, age of data, etc.
  • most any desired criteria can be employed by the validation component 804 to control access to the data network.
  • this component can include a relationship analysis component 910 and a location determining component 912.
  • these two subcomponents (910, 912) can facilitate intelligent clustering and/or placement of a data element within the data network.
  • the relationship analysis component 910 can, based upon descriptive attributes and/or metadata, identify relationships ⁇ e.g., parallels, patterns, trends, etc) between a subject element and other elements maintained within the data network.
  • the location determining component 912 can employ this information to intelligently and/or logically cluster or place the data within the data network.
  • FIG. 10 illustrates an alternative block diagram of an example data organization component 702 that employs a machine learning and reasoning (MLR) component 702 which facilitates automating one or more features in accordance with the subject innovation.
  • MLR machine learning and reasoning
  • the subject innovation can employ various MLR-based schemes for carrying out various aspects thereof. For example, a process for determining what criteria should be employed when determining the smallest meaningful bit of information can be facilitated via an automatic classifier system and process.
  • the classifier can be employed to determine which location should be selected in order to effectively cluster and/or store data elements to optimize usability, traversal and/or mining operations.
  • Such classification can employ a probabilistic and/or statistical- based analysis (e.g. , factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed.
  • a support vector machine is an example of a classifier that can be employed.
  • the SVM operates by finding a hypersurface in the space of possible inputs, which the hypersurface attempts to split the triggering criteria from the non- triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data.
  • Other directed and undirected model classification approaches include, e.g., na ⁇ ve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.
  • the subject innovation can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g. , via observing user behavior, receiving extrinsic information).
  • SVM 's are configured via a learning or training phase within a classifier constructor and feature selection module.
  • the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to a predetermined criteria when to gather data, what granularity to use with regard to tagging, how to determine meaningful bits, where to store data elements to enhance usability, etc.
  • FIG. 11 there is illustrated a block diagram of a computer operable to execute the disclosed architecture.
  • FIG. 11 and the following discussion are intended to provide a brief, general description of a suitable computing environment 1100 in which the various aspects of the innovation can be implemented. While the innovation has been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software.
  • program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types.
  • inventive methods can be practiced with other computer system configurations, including single- processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operative Iy coupled to one or more associated devices.
  • the illustrated aspects of the innovation may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network.
  • program modules can be located in both local and remote memory storage devices.
  • a computer typically includes a variety of computer-readable media.
  • Computer-readable media can be any available media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and nonremovable media.
  • Computer-readable media can comprise computer storage media and communication media.
  • Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.
  • Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media.
  • modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
  • communication media includes wired media such as a wired network or direct- wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media.
  • the exemplary environment 1100 for implementing various aspects of the innovation includes a computer 1102, the computer 1102 including a processing unit 1104, a system memory 1106 and a system bus 1108.
  • the system bus 1108 couples system components including, but not limited to, the system memory 1106 to the processing unit 1104.
  • the processing unit 1104 can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit 1104.
  • the system bus 1108 can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures.
  • the system memory 1106 includes read-only memory (ROM) 1110 and random access memory (RAM) 1112.
  • ROM read-only memory
  • RAM random access memory
  • a basic input/output system (BIOS) is stored in a non-volatile memory 1110 such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 1102, such as during start-up.
  • the RAM 1112 can also include a high-speed RAM such as static RAM for caching data.
  • the computer 1102 further includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA), which internal hard disk drive 1114 may also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) 1116, (e.g. , to read from or write to a removable diskette 1118) and an optical disk drive 1120, (e.g., reading a CD-ROM disk 1122 or, to read from or write to other high capacity optical media such as the DVD).
  • the hard disk drive 1114, magnetic disk drive 1116 and optical disk drive 1120 can be connected to the system bus 1108 by a hard disk drive interface 1124, a magnetic disk drive interface 1126 and an optical drive interface 1128, respectively.
  • the interface 1124 for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject innovation.
  • the drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth.
  • the drives and media accommodate the storage of any data in a suitable digital format.
  • computer-readable media refers to a HDD, a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and further, that any such media may contain computer-executable instructions for performing the methods of the innovation.
  • a number of program modules can be stored in the drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134 and program data 1136. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 1112. It is appreciated that the innovation can be implemented with various commercially available operating systems or combinations of operating systems.
  • a user can enter commands and information into the computer 1102 through one or more wired/wireless input devices, e.g., a keyboard 1138 and a pointing device, such as a mouse 1140.
  • Other input devices may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like.
  • a monitor 1144 or other type of display device is also connected to the system bus 1108 via an interface, such as a video adapter 1146.
  • a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.
  • the computer 1102 may operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 1148.
  • the remote computer(s) 1148 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1102, although, for purposes of brevity, only a memory/storage device 1150 is illustrated.
  • the logical connections depicted include wired/wireless connectivity to a local area network (LAN) 1152 and/or larger networks, e.g., a wide area network (WAN) 1154.
  • LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, e.g., the Internet.
  • the computer 1102 When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156.
  • the adapter 1156 may facilitate wired or wireless communication to the LAN 1152, which may also include a wireless access point disposed thereon for communicating with the wireless adapter 1156.
  • the computer 1102 can include a modem 1158, or is connected to a communications server on the WAN 1154, or has other means for establishing communications over the WAN 1154, such as by way of the Internet.
  • the modem 1158 which can be internal or external and a wired or wireless device, is connected to the system bus 1108 via the serial port interface 1142.
  • program modules depicted relative to the computer 1102, or portions thereof, can be stored in the remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.
  • the computer 1102 is operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g. , a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone.
  • any wireless devices or entities operatively disposed in wireless communication e.g. , a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone.
  • the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.
  • Wi-Fi Wireless Fidelity
  • Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station.
  • Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity.
  • IEEE 802.11 a, b, g, etc.
  • a Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which use IEEE 802.3 or Ethernet).
  • Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps (802.1 Ia) or 54 Mbps (802.1 Ib) data rate, for example, or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic lOBaseT wired Ethernet networks used in many offices.
  • the system 1200 includes one or more client(s) 1202.
  • the client(s) 1202 can be hardware and/or software (e.g., threads, processes, computing devices).
  • the client(s) 1202 can house cookie(s) and/or associated contextual information by employing the innovation, for example.
  • the system 1200 also includes one or more server(s) 1204.
  • the server(s) 1204 can also be hardware and/or software (e.g., threads, processes, computing devices).
  • the servers 1204 can house threads to perform transformations by employing the innovation, for example.
  • One possible communication between a client 1202 and a server 1204 can be in the form of a data packet adapted to be transmitted between two or more computer processes.
  • the data packet may include a cookie and/or associated contextual information, for example.
  • the system 1200 includes a communication framework 1206 (e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s) 1202 and the server(s) 1204.
  • a communication framework 1206 e.g., a global communication network such as the Internet
  • Communications can be facilitated via a wired (including optical fiber) and/or wireless technology.
  • the client(s) 1202 are operatively connected to one or more client data store(s) 1208 that can be employed to store information local to the client(s) 1202 (e.g., cookie(s) and/or associated contextual information).
  • the server(s) 1204 are operatively connected to one or more server data store(s) 1210 that can be employed to store information local to the servers 1204.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
  • Communication Control (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention a pour objet un système qui peut permettre l'établissement d'un réseau de données d'auto-description. Globalement, l'innovation propose un mécanisme grâce auquel des données d'auto-description peuvent être collectées, validées et stockées de manière à permettre à chaque élément de données d'être par nature auto-descriptif. La manière dont les données sont stockées peut s'apparenter à une 'chimie de données', les données étant stockées dans le plus petit bit significatif (par exemple, atome) couplé aux métadonnées descriptives (par exemple, balises). Dans un exemple spécifique, le réseau de données maintient les données liées à la santé où chaque élément comprend un élément de données central enveloppé avec les métadonnées descriptives. Les métadonnées descriptives (par exemple, balises) peuvent être employées pour interrelier les éléments de données à stocker ainsi que pour faciliter le parcours du réseau de données dans son ensemble.
PCT/US2008/054812 2007-02-23 2008-02-22 Structure de données d'auto-description WO2008103969A1 (fr)

Applications Claiming Priority (2)

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US11/678,266 US8615404B2 (en) 2007-02-23 2007-02-23 Self-describing data framework
US11/678,266 2007-02-23

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CL (1) CL2008000533A1 (fr)
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